This disclosure relates to energy management, and more particularly to electrical device control methods and electrical energy consumption systems. The disclosure finds particular application to energy management of appliances, for example, dishwashers, clothes washers, dryers, HVAC systems, etc.
In order to reduce high peak power demand, many utilities have instituted time of use (TOU) metering and rates, which include higher rates for energy usage during on-peak times and lower rates for energy usage during off-peak times. As a result, consumers are provided with an incentive to use electricity at off-peak times rather than on-peak times and to reduce overall energy consumption of appliances at all times.
Utility power systems become “smart” and demand response enabled by employing a head end management system, such as a company or program responsible for monitoring and running a demand response program. This usually requires equipment and time investments by utilities to install automatic meter reading (AMR) systems, advanced metering infrastructure, or other types of “smart” utility meters in each home. AMR systems, for example, provide for automatically collecting consumption, diagnostic, and status data from water meter or energy metering devices (water, gas, electric) and transferring that data to a central database for billing, troubleshooting, and analyzing. AMI represents the networking technology of fixed network meter systems that go beyond AMR into remote utility management. The meters in an AMI system are often referred to as smart meters, since they can use collected data based on programmed logic.
Smart grid applications improve the ability of electricity producers and consumers to communicate with one another and make decisions about how and when to produce and consume power. Demand response (DR) technology, for example, allows customers to shift from an event based demand response where the utility requests the shedding of load, towards a more 24/7 based demand response where the customer sees incentives for controlling load all the time. One advantage of a smart grid application is time-based pricing. Customers who traditionally pay a fixed rate for kWh and kW/month can set their threshold and adjust their usage to take advantage of fluctuating prices. Another advantage, is being able to closely monitor, shift, and balance load in a way that allows the customer to save peak load and not only save on kWh and kW/month but be able to trade what they have saved in an energy market. However, this involves sophisticated energy management systems, incentives, and a viable trading market.
When TOU or DR events occur, a number of users turning appliances on at the same time can create an initial influx of power that is up to several times the normal load on a power grid. This initial influx could compromise a power grid as well as cause it to be fully loaded, and thus, cause a reduction or shut off in power temporarily (e.g., brown outs or black outs). In addition, expenditures to run outside “peaker plants” are costly and may not be as environmentally friendly.
Therefore, a need exists to provide a method and system to run demand response systems without a head end investment, such as acquiring smart meters or two-way communication. Utilities have a need to instruct segregated populations of power consuming devices to enable them to limit peak load and/or smooth payback spikes for saving money and avoiding power outages without requiring smart meter employment.